Code
library(Rcpp)
cppFunction('
int GCD(int a, int b) {
return std::gcd(a, b);
}
int LCM(int a, int b) {
return abs(a * b) / GCD(a, b);
}
')HW5
This is the link to my repo.
Problem 1 - OOP
a.
Code
##' S4 Class for Rational Numbers
##'
##' The `rational` class represents a rational number with a numerator and denominator.
##'
##' @slot numerator An integer representing the numerator.
##' @slot denominator An integer representing the denominator. Must be non-zero.
setClass(
"rational",
slots = c(numerator = "integer", denominator = "integer"),
validity = function(object) {
if (object@denominator == 0) {
return("Denominator cannot be zero.")
}
TRUE
}
)
rational <- function(numerator, denominator = 1L) {
if (denominator == 0) {
stop("Denominator cannot be zero.")
}
new("rational", numerator = as.integer(numerator), denominator = as.integer(denominator))
}
setMethod("show", "rational", function(object) {
cat(sprintf("%d/%d\n", object@numerator, object@denominator))
})
simplify <- function(r) {
gcd <- GCD(r@numerator, r@denominator)
new("rational",
numerator = as.integer(r@numerator / gcd),
denominator = as.integer(r@denominator / gcd))
}
quotient <- function(r, digits = getOption("digits")) {
value <- r@numerator / r@denominator
formatted_value <- format(value, digits = digits)
print(formatted_value)
invisible(value)
}
setMethod("+", c("rational", "rational"), function(e1, e2) {
num <- e1@numerator * e2@denominator + e2@numerator * e1@denominator
denom <- e1@denominator * e2@denominator
simplify(rational(num, denom))
})
setMethod("-", c("rational", "rational"), function(e1, e2) {
num <- e1@numerator * e2@denominator - e2@numerator * e1@denominator
denom <- e1@denominator * e2@denominator
simplify(rational(num, denom))
})
setMethod("*", c("rational", "rational"), function(e1, e2) {
num <- e1@numerator * e2@numerator
denom <- e1@denominator * e2@denominator
simplify(rational(num, denom))
})
setMethod("/", c("rational", "rational"), function(e1, e2) {
if (e2@numerator == 0) {
stop("Division by zero.")
}
num <- e1@numerator * e2@denominator
denom <- e1@denominator * e2@numerator
simplify(rational(num, denom))
})b.
Code
r1 <- rational(12, 15)
r2 <- rational(7, 11)
r3 <- rational(0, 8)Code
r112/15Code
r30/8Code
r1 + r279/55Code
r1 - r29/55Code
r1 * r228/55Code
r1 / r244/35Code
r1 + r34/5Code
r1 * r30/1Code
r2 / r3Error in r2/r3: Division by zero.Code
quotient(r1)[1] "0.8"Code
quotient(r2)[1] "0.6363636"Code
quotient(r2, digits = 3)[1] "0.636"Code
quotient(r2, digits = 3.14)[1] "0.636"Code
quotient(r2, digits = "avocado")Warning in prettyNum(.Internal(format(x, trim, digits, nsmall, width, 3L, : NAs
introduced by coercionError in prettyNum(.Internal(format(x, trim, digits, nsmall, width, 3L, : invalid value -2147483648 for 'digits' argumentCode
q2 <- quotient(r2, digits = 3)[1] "0.636"Code
q2[1] 0.6363636Code
quotient(r3)[1] "0"Code
simplify(r1)4/5Code
simplify(r2)7/11Code
simplify(r3)0/1c.
Code
r4 <- rational(4,0)Error in rational(4, 0): Denominator cannot be zero.Code
r4 <- rational(4,3,2)Error in rational(4, 3, 2): unused argument (2)Code
r4 <- rational('4','3')
r44/3Code
r4 <- rational('4/3')Warning in initialize(value, ...): NAs introduced by coercionCode
r4 <- rational(1.5,4.3)
r41/4Problem 2 - plotly
a.
Code
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.0
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsCode
library(ggplot2)
art_sales <- read.csv("df_for_ml_improved_new_market.csv")
art_sales <- art_sales %>%
pivot_longer(cols = starts_with("Genre___"), names_to = "Genre", values_to = "Value") %>%
filter(Value == 1) %>%
mutate(Genre = str_replace(Genre, "Genre___", "")) %>%
select(-Value)
ggplot(art_sales, aes(x = factor(year), fill = Genre)) +
geom_bar(position = "fill") +
scale_y_continuous(labels = scales::percent_format()) +
labs(title = "Distribution of Genre of Sales Across Years", x = "Year", y = "Proportion of Sales") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
b.
Code
library(plotly)
Attaching package: 'plotly'The following object is masked from 'package:ggplot2':
last_plotThe following object is masked from 'package:stats':
filterThe following object is masked from 'package:graphics':
layoutCode
library(dplyr)Code
interactive_data <- art_sales %>%
group_by(year, Genre) %>%
summarize(median_price = median(price_usd, na.rm = TRUE), .groups = "drop")Code
interactive_plot <- plot_ly(
data = interactive_data,
x = ~year,
y = ~median_price,
color = ~Genre,
type = 'scatter',
mode = 'lines+markers',
hoverinfo = 'text',
text = ~paste("Year:", year, "<br>Genre:", Genre, "<br>Median Price (USD):", median_price)
) %>%
layout(
title = "Sales Price in USD Over Time by Genre",
xaxis = list(title = "Year"),
yaxis = list(title = "Median Price (USD)"),
legend = list(title = list(text = "Genre")),
hovermode = "closest"
)
interactive_plotProblem 3 - data.table
Code
library(data.table)
Attaching package: 'data.table'The following objects are masked from 'package:lubridate':
hour, isoweek, mday, minute, month, quarter, second, wday, week,
yday, yearThe following objects are masked from 'package:dplyr':
between, first, lastThe following object is masked from 'package:purrr':
transposeCode
library(nycflights13)Code
flights_dt <- as.data.table(flights)
airports_dt <- as.data.table(airports)
planes_dt <- as.data.table(planes)a.
Code
departure_delay_dt <- flights_dt[, .(
mean_departure_delay = mean(dep_delay, na.rm = TRUE),
median_departure_delay = median(dep_delay, na.rm = TRUE),
flight_count = .N
), by = .(dest)][flight_count >= 10]
departure_delay_dt <- merge(departure_delay_dt, airports_dt, by.x = "dest", by.y = "faa")
departure_delay_dt <- departure_delay_dt[
order(-mean_departure_delay),
.(name, mean_departure_delay, median_departure_delay)
]
print(departure_delay_dt) name mean_departure_delay
<char> <num>
1: Columbia Metropolitan 35.570093
2: Tulsa Intl 34.906355
3: Will Rogers World 30.568807
4: Birmingham Intl 29.694853
5: Mc Ghee Tyson 28.493955
6: Jackson Hole Airport 26.545455
7: Des Moines Intl 26.232955
8: Richmond Intl 23.639847
9: Albany Intl 23.620525
10: Dane Co Rgnl Truax Fld 23.580071
11: Cherry Capital Airport 22.083333
12: Theodore Francis Green State 21.765363
13: Charlottesville-Albemarle 21.391304
14: South Bend Rgnl 21.100000
15: Manchester Regional Airport 21.024678
16: Akron Canton Regional Airport 20.820878
17: San Antonio Intl 20.737463
18: Kansas City Intl 20.326477
19: Eppley Afld 20.205596
20: Gerald R Ford Intl 19.533333
21: Cincinnati Northern Kentucky Intl 19.520321
22: Bangor Intl 19.475000
23: Wilmington Intl 19.416667
24: Piedmont Triad 19.398000
25: Greenville-Spartanburg International 19.261965
26: General Mitchell Intl 18.760118
27: Sacramento Intl 18.691489
28: Chicago Midway Intl 18.589763
29: Savannah Hilton Head Intl 18.260292
30: Bradley Intl 17.720874
31: Montrose Regional Airport 17.642857
32: Norfolk Intl 17.576389
33: James M Cox Dayton Intl 17.498573
34: Yeager 17.000000
35: Washington Dulles Intl 16.982935
36: Jacksonville Intl 16.484434
37: Portland Intl Jetport 16.455773
38: Louisville International Airport 16.426141
39: Baltimore Washington Intl 16.396816
40: Portland Intl 16.255935
41: Greater Rochester Intl 16.243861
42: Lambert St Louis Intl 16.010557
43: Nashville Intl 15.982471
44: Myrtle Beach Intl 15.758621
45: Memphis Intl 15.658796
46: Eagle Co Rgnl 15.495192
47: Denver Intl 15.156228
48: Charleston Afb Intl 14.694775
49: Syracuse Hancock Intl 14.444769
50: William P Hobby 14.340191
51: Louis Armstrong New Orleans Intl 14.249463
52: Indianapolis Intl 14.046710
53: Albuquerque International Sunport 13.740157
54: Pittsburgh Intl 13.704966
55: Burlington Intl 13.605651
56: Chicago Ohare Intl 13.570484
57: Bob Hope 13.475676
58: Buffalo Niagara Intl 13.423951
59: Cleveland Hopkins Intl 13.387931
60: Metropolitan Oakland Intl 13.344051
61: Minneapolis St Paul Intl 13.324806
62: Austin Bergstrom Intl 13.025641
63: Palm Beach Intl 12.992918
64: San Francisco Intl 12.866289
65: Fort Lauderdale Hollywood Intl 12.731104
66: Hartsfield Jackson Atlanta Intl 12.509824
67: Raleigh Durham Intl 12.445870
68: Yampa Valley 12.285714
69: Port Columbus Intl 12.223787
70: Tampa Intl 12.135007
71: Philadelphia Intl 11.998709
72: Detroit Metro Wayne Co 11.812252
73: Gallatin Field 11.457143
74: Orlando Intl 11.275998
75: Long Beach 11.183735
76: San Diego Intl 11.110866
77: George Bush Intercontinental 10.842179
78: Seattle Tacoma Intl 10.725922
79: Phoenix Sky Harbor Intl 10.412926
80: Ronald Reagan Washington Natl 10.293000
81: Norman Y Mineta San Jose Intl 10.103659
82: Mc Carran Intl 9.418819
83: Los Angeles Intl 9.401344
84: Honolulu Intl 9.289362
85: Charlotte Douglas Intl 9.222879
86: Salt Lake City Intl 9.026851
87: Miami Intl 8.876558
88: General Edward Lawrence Logan Intl 8.730613
89: Dallas Fort Worth Intl 8.681909
90: Southwest Florida Intl 8.275577
91: Asheville Regional Airport 8.190114
92: John Wayne Arpt Orange Co 7.761905
93: Sarasota Bradenton Intl 7.256027
94: Martha\\\\'s Vineyard 7.051643
95: NW Arkansas Regional 6.464886
96: Nantucket Mem 6.456604
97: Key West Intl 3.647059
98: Palm Springs Intl -2.944444
name mean_departure_delay
median_departure_delay
<num>
1: 14.0
2: 8.0
3: 10.0
4: 1.0
5: 0.0
6: 13.5
7: -1.0
8: -1.0
9: 1.0
10: -1.0
11: -3.0
12: 0.0
13: -2.5
14: 14.0
15: 0.0
16: 0.0
17: 1.0
18: -1.0
19: -1.0
20: -1.0
21: -2.0
22: -2.0
23: -3.0
24: -1.0
25: -1.0
26: 0.0
27: 2.0
28: 2.0
29: -1.0
30: -1.0
31: 3.0
32: -2.0
33: -2.0
34: -4.0
35: -2.0
36: -1.0
37: -2.0
38: -2.0
39: -2.0
40: 1.0
41: -2.0
42: -1.0
43: -1.0
44: -1.0
45: -1.0
46: -1.0
47: 1.0
48: -2.0
49: -2.0
50: 0.0
51: -2.0
52: -2.0
53: 0.0
54: -2.0
55: -2.0
56: -2.0
57: -1.0
58: -2.0
59: -2.0
60: 0.0
61: -2.0
62: -1.0
63: 0.0
64: 0.0
65: -1.0
66: -2.0
67: -2.0
68: 6.5
69: -3.0
70: -1.0
71: -3.0
72: -3.0
73: 0.0
74: -1.0
75: -1.0
76: 0.0
77: 0.0
78: -1.0
79: -1.0
80: -3.0
81: -1.0
82: -1.0
83: -1.0
84: -1.0
85: -3.0
86: -1.0
87: -2.0
88: -3.0
89: -3.0
90: -2.0
91: -3.0
92: -1.0
93: -3.0
94: -2.0
95: -5.0
96: -3.0
97: 0.0
98: -4.0
median_departure_delayCode
arrival_delay_dt <- flights_dt[, .(
mean_arrival_delay = mean(arr_delay, na.rm = TRUE),
median_arrival_delay = median(arr_delay, na.rm = TRUE),
flight_count = .N
), by = .(dest)][flight_count >= 10]
arrival_delay_dt <- merge(arrival_delay_dt, airports_dt, by.x = "dest", by.y = "faa")
arrival_delay_dt <- arrival_delay_dt[
order(-mean_arrival_delay),
.(name, mean_arrival_delay, median_arrival_delay)
]
print(arrival_delay_dt) name mean_arrival_delay
<char> <num>
1: Columbia Metropolitan 41.76415094
2: Tulsa Intl 33.65986395
3: Will Rogers World 30.61904762
4: Jackson Hole Airport 28.09523810
5: Mc Ghee Tyson 24.06920415
6: Dane Co Rgnl Truax Fld 20.19604317
7: Richmond Intl 20.11125320
8: Akron Canton Regional Airport 19.69833729
9: Des Moines Intl 19.00573614
10: Gerald R Ford Intl 18.18956044
11: Birmingham Intl 16.87732342
12: Theodore Francis Green State 16.23463687
13: Greenville-Spartanburg International 15.93544304
14: Cincinnati Northern Kentucky Intl 15.36456376
15: Savannah Hilton Head Intl 15.12950601
16: Manchester Regional Airport 14.78755365
17: Eppley Afld 14.69889841
18: Yeager 14.67164179
19: Kansas City Intl 14.51405836
20: Albany Intl 14.39712919
21: General Mitchell Intl 14.16722038
22: Piedmont Triad 14.11260054
23: Washington Dulles Intl 13.86420212
24: Cherry Capital Airport 12.96842105
25: James M Cox Dayton Intl 12.68048606
26: Louisville International Airport 12.66938406
27: Chicago Midway Intl 12.36422360
28: Sacramento Intl 12.10992908
29: Jacksonville Intl 11.84483416
30: Nashville Intl 11.81245891
31: Portland Intl Jetport 11.66040210
32: Greater Rochester Intl 11.56064461
33: Hartsfield Jackson Atlanta Intl 11.30011285
34: Lambert St Louis Intl 11.07846451
35: Norfolk Intl 10.94909344
36: Baltimore Washington Intl 10.72673385
37: Memphis Intl 10.64531435
38: Port Columbus Intl 10.60132291
39: Charleston Afb Intl 10.59296847
40: Philadelphia Intl 10.12719014
41: Raleigh Durham Intl 10.05238095
42: Indianapolis Intl 9.94043412
43: Charlottesville-Albemarle 9.50000000
44: Cleveland Hopkins Intl 9.18161129
45: Ronald Reagan Washington Natl 9.06695204
46: Burlington Intl 8.95099602
47: Buffalo Niagara Intl 8.94595186
48: Syracuse Hancock Intl 8.90392501
49: Denver Intl 8.60650021
50: Palm Beach Intl 8.56297210
51: Bob Hope 8.17567568
52: Fort Lauderdale Hollywood Intl 8.08212154
53: Bangor Intl 8.02793296
54: Asheville Regional Airport 8.00383142
55: Pittsburgh Intl 7.68099053
56: Gallatin Field 7.60000000
57: NW Arkansas Regional 7.46572581
58: Tampa Intl 7.40852503
59: Charlotte Douglas Intl 7.36031885
60: Minneapolis St Paul Intl 7.27016886
61: William P Hobby 7.17618819
62: Bradley Intl 7.04854369
63: San Antonio Intl 6.94537178
64: South Bend Rgnl 6.50000000
65: Louis Armstrong New Orleans Intl 6.49017497
66: Key West Intl 6.35294118
67: Eagle Co Rgnl 6.30434783
68: Austin Bergstrom Intl 6.01990875
69: Chicago Ohare Intl 5.87661475
70: Orlando Intl 5.45464309
71: Detroit Metro Wayne Co 5.42996346
72: Portland Intl 5.14157973
73: Nantucket Mem 4.85227273
74: Wilmington Intl 4.63551402
75: Myrtle Beach Intl 4.60344828
76: Albuquerque International Sunport 4.38188976
77: George Bush Intercontinental 4.24079040
78: Norman Y Mineta San Jose Intl 3.44817073
79: Southwest Florida Intl 3.23814963
80: San Diego Intl 3.13916574
81: Sarasota Bradenton Intl 3.08243131
82: Metropolitan Oakland Intl 3.07766990
83: General Edward Lawrence Logan Intl 2.91439222
84: San Francisco Intl 2.67289152
85: Yampa Valley 2.14285714
86: Phoenix Sky Harbor Intl 2.09704733
87: Montrose Regional Airport 1.78571429
88: Los Angeles Intl 0.54711094
89: Dallas Fort Worth Intl 0.32212685
90: Miami Intl 0.29905978
91: Mc Carran Intl 0.25772849
92: Salt Lake City Intl 0.17625459
93: Long Beach -0.06202723
94: Martha\\\\'s Vineyard -0.28571429
95: Seattle Tacoma Intl -1.09909910
96: Honolulu Intl -1.36519258
97: John Wayne Arpt Orange Co -7.86822660
98: Palm Springs Intl -12.72222222
name mean_arrival_delay
median_arrival_delay
<num>
1: 28.0
2: 14.0
3: 16.0
4: 15.0
5: 2.0
6: 1.0
7: 1.0
8: 3.0
9: 0.0
10: 1.0
11: -2.0
12: 1.0
13: -0.5
14: -3.0
15: -1.0
16: -3.0
17: -2.0
18: -1.5
19: 0.0
20: -4.0
21: 0.0
22: -2.0
23: -3.0
24: -10.0
25: -3.0
26: -2.0
27: -1.0
28: 4.0
29: -2.0
30: -2.0
31: -4.0
32: -5.0
33: -1.0
34: -3.0
35: -4.0
36: -5.0
37: -2.5
38: -3.0
39: -4.0
40: -3.0
41: -3.0
42: -3.0
43: -5.0
44: -5.0
45: -2.0
46: -4.0
47: -5.0
48: -5.0
49: -2.0
50: -3.0
51: -3.0
52: -3.0
53: -9.0
54: -1.0
55: -5.0
56: -2.0
57: -2.0
58: -4.0
59: -3.0
60: -5.0
61: -4.0
62: -10.0
63: -9.0
64: -3.5
65: -6.0
66: 7.0
67: -4.0
68: -5.0
69: -8.0
70: -5.0
71: -7.0
72: -5.0
73: -3.0
74: -7.0
75: -13.0
76: -5.5
77: -5.0
78: -7.0
79: -5.0
80: -5.0
81: -5.0
82: -9.0
83: -9.0
84: -8.0
85: 2.0
86: -6.0
87: -10.5
88: -7.0
89: -9.0
90: -9.0
91: -8.0
92: -8.0
93: -10.0
94: -11.0
95: -11.0
96: -7.0
97: -11.0
98: -13.5
median_arrival_delayb.
Code
fastest_aircraft_dt <- flights_dt[
!is.na(air_time),
.(avg_speed = mean(distance / (air_time / 60), na.rm = TRUE), flight_count = .N),
by = .(tailnum)
][order(-avg_speed)][1]
fastest_aircraft_dt <- merge(fastest_aircraft_dt, planes_dt, by = "tailnum")
fastest_aircraft_dt <- fastest_aircraft_dt[, .(model, avg_speed, flight_count)]
fastest_aircraft_dt model avg_speed flight_count
<char> <num> <int>
1: 777-222 500.8163 1